RESEARCH DISCIPLINE
Statistical Analysis for Finance Research
Consulting for Asset Pricing & Corporate Studies
Finance research statistical analysis for stock returns, fund performance, event studies and corporate finance panels, with heavy tails, volatility and look-ahead bias examined before any result is reported and the output prepared for journal review.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
Statistical Analysis for Finance Research
Finance research statistical analysis is the statistical design, estimation and reporting of studies of asset prices, returns, funds, intermediaries and corporate financing decisions, from daily security data and monthly fund returns to firm-level panels. Returns have heavy tails and volatility that changes over time, observations are correlated across periods and across securities on the same date, and apparent performance depends on what an investor could have known and which entities were still listed, so a model that assumes normal independent errors and evaluates a strategy on data shaped by its own selection yields results that rarely survive out-of-sample. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, provides financial data analysis through in-house PhD-level statisticians, who settle the sample, the timing of information and the error structure with you before any model is estimated.
WHEN TO ENGAGE
Statistical Support at Different Stages of Finance Research
Finance projects reach us before a test is designed, once a signal or a panel is ready to estimate, or after a referee doubts a backtest.
Designing an Empirical Test You are planning an asset pricing, event or corporate finance study and need the sample, the return window, the benchmark and the main specification fixed before results are seen.
Testing a Signal or Strategy You have a return predictor or portfolio backtest and need the estimation and holdout periods, the treatment of delistings and of overlapping returns set up and checked.
Estimating a Firm or Fund Panel Your firm-quarter or fund-month panel is assembled, and the effects, the two-way clustering and the treatment of extreme values have to be settled before any estimate is read.
Responding to a Review A referee or committee has questioned look-ahead in a variable, survivorship in the sample or the standard errors, and each point needs an answer grounded in reestimated results.
RESEARCH CONTEXT
Research Designs and Data We Work With in Finance Research
Financial data arrive as long return series, fund records and firm panels, and each has a dependence structure that decides how its estimates can be trusted.
01
Daily and Monthly Return Panels
Returns for many securities over many days or months, correlated across securities on the same date and with volatility that changes over time.
02
Fund and Portfolio Performance
Monthly fund returns and holdings in which funds close or merge, with performance measured against a benchmark or a factor model.
03
Event Studies
Abnormal returns around announcements such as earnings or mergers, where event windows can overlap and firms cluster in calendar time.
04
Corporate Finance Panels
Capital structure, investment and payout decisions observed by firm-quarter, where financing choices respond to the same conditions as the outcome.
05
Backtests and Prediction Studies
Trading signals and forecasting models tested on historical data, where the information available at each date decides whether the test is valid.
06
Credit and Risk Records
Default, rating and loss records together with market risk measures, where events are rare and extreme losses matter more than the average.
METHODOLOGICAL CONSIDERATIONS
Statistical Issues We Consider in Finance Research
These checks come first in an empirical finance study, because how a sample was built and when information became known can change a result more than the model does.
Fat Tails and Changing Volatility Returns have extreme observations and volatility that clusters, so normal-error tests mislead. Robust inference, volatility models or resampling are used, and results are checked without the most extreme values.
Look-Ahead Bias Each variable is dated by when it was publicly known, such as accounting data after the filing. A signal built on information not yet available makes a backtest look better than any strategy that could be run.
Survivorship and Delisting Samples that keep only the funds or firms that survived overstate average performance. Delisted and merged entities are retained, and the treatment of their final returns is stated.
Overlapping Returns and Common Shocks Multi-period returns overlap and securities move together on the same date, so errors are correlated in both directions. Standard errors allow for firm and time dependence, or regressions are run date by date.
Data Snooping Many signals, windows and specifications are tried before one is reported. The number of tests is recorded, and evidence is judged against a stricter standard or a holdout period that was left untouched.
Economic Versus Statistical Size A statistically significant return difference can disappear after transaction costs and risk adjustment. Estimates are given in return units with intervals and set against the costs of trading.
STATISTICAL METHODS
Methods Selected for the Research Question and Study Design
Each method family is chosen for the outcome and the data structure of your study, and the line under each title explains why it is used in financial econometrics.
Longitudinal & Multilevel Analysis
Finance panel data analysis of firm-quarter and fund-month records needs entity and time effects with clustered errors, and financial time series analysis of daily returns models changing volatility.
Regression & Predictive Modeling
Return and risk regressions use robust errors, distress and default events are modeled by logistic regression, and a predictive model is judged on a period it was not fitted to.
Bayesian & Computational Methods
Bootstrap intervals suit heavy-tailed returns, and simulation shows how often searching many signals would produce the observed best performance by chance alone.
Clinical, Survival & Epidemiological
Time until default, delisting or fund closure is censored for entities still active, and a merger and a failure are competing ends, so hazard models use the whole sample.
Comparative & Hypothesis Testing
Abnormal returns are tested against zero with errors that allow for events clustered in calendar time, portfolios are compared before and after an event, and a signed-rank test checks that a few extreme returns do not drive the result.
Multivariate, Scale & Reliability
Many correlated predictors or yields are summarized by principal components before they enter a return model, and their correlations are examined instead of entering dozens of regressors.
DELIVERABLES
Statistical Outputs Prepared for Your Research
The outputs follow the structure of an empirical finance study, from the review of how the sample was built to the scripts that regenerate every reported estimate.
Data & Study Review The sample construction, security identifiers, delisting treatment, the dates on which each variable became known and the pattern of missing observations are reviewed before estimation.
Statistical Analysis The agreed panel, event-study, return-regression or hazard models, with tests for heteroskedasticity, autocorrelation and stationarity and the planned subperiod and holdout checks.
Effect Estimates & Uncertainty Coefficients, abnormal returns and performance differences in return units, with confidence intervals from standard errors that allow for dependence over time and across securities.
Tables & Figures Summary statistics for the sample, regression tables with alternative specifications, event-window and cumulative return figures, and portfolio sort tables where they are used.
Statistical Interpretation Each result is explained in financial terms with its economic size and the limits of the sample, and a backtest is not described as a tradable strategy unless costs and timing are included.
Analysis Files R or Python scripts, Stata do-files or SAS programs, with the data construction, variable timing and random seeds documented so that every table can be regenerated.
REPORTING
Reporting Finance Research
Finance journals share no checklist, so referees judge sample construction, variable timing and inference against each journal's own policy.
Many finance journals now ask authors to deposit data and code, and the policies differ by journal and by data license, so the target journal’s requirements are checked when the analysis is planned. The sample is documented step by step, including the treatment of delistings, outliers and missing values, and the dates on which variables were assumed known are stated, since referees look for look-ahead bias first. Estimates appear in return units with confidence intervals, the number of specifications and signals examined is disclosed, and alternative specifications are shown side by side with the standard errors that match the dependence in the data.
ILLUSTRATIVE EXAMPLE
How the Analysis May Look in Practice
An illustration of how a performance question about funds moves from a return database to a result that allows for luck.
ILLUSTRATIVE EXAMPLE · MUTUAL FUND PERFORMANCE RESEARCH · UNITED STATES
A finance researcher wanted to know whether actively managed funds with stronger past performance went on to earn higher risk-adjusted returns, using monthly fund returns.
Funds close and merge, so a sample of survivors would overstate performance, and with many funds some will look skilled by chance. Before any result was examined, we kept the funds that closed, fixed the factor model that defined risk-adjusted return and set the formation and holdout windows. Standard errors allowed for dependence within funds and across funds in the same month, and a bootstrap across funds produced the distribution of alphas expected from luck alone, against which the observed distribution was compared. Returns were measured net of fees, and the result was worded as persistence or its absence in this sample, not as proof of manager skill.
Outcome: The researcher received the difference in risk-adjusted return between past-performance groups with its 95% confidence interval, the bootstrap comparison with luck, survivorship-adjusted sample tables, a methods paragraph and the scripts.
This example does not describe a real client or project. We never share details of actual client work, in line with the confidentiality we promise every researcher. This illustrative example is included to show the type, scope, and standard of work involved in this kind of analysis.
RESEARCHER VOICE
What Researchers Say
Feedback from a finance researcher on not having to explain the field first, on the usual data problems and on clustered observations handled from the start.
“What worked for me was that I did not have to translate the whole Finance context before discussing the statistics. They understood the study design, the usual data problems, and the reporting expectations. We were particularly concerned about the clustered structure of the data rather than treating every row as independent, and that was dealt with properly from the start.”
HOW WE ASSESS YOUR PROJECT
We Review Your Actual Research
Your assessment is based on a review of your actual research materials and analytical requirements, not a generic project category.
You Submit Your Project Details
Share whatever is available at the current stage, such as the research aim, protocol or proposal, dataset, variable list, manuscript or statistical requirements.
We Review Your Research and Define the Scope
Your statistically trained Research Coordinator reviews the materials to understand the study design, research questions, data structure, analytical requirements and expected deliverables. If clarification is needed, additional documents or information can be requested before the assessment is finalized.
Your Analysis Is Completed and Delivered
Once the scope and fixed project fee are agreed, the in-house statistician completes the work and the Research Coordinator remains the communication point through delivery, with the analysis, deliverables and timeline as agreed.
SOFTWARE
Statistical Software for Finance Research
Financial analyses are run in the language or package your group already uses, with syntax or scripts provided where applicable.
Statistical Support Within Your Research OnlineSPSS provides the agreed methodological, analytical, and statistical reporting support. The researcher or research team retains the scientific question, substantive interpretation, conclusions, and overall responsibility for the research.
FREQUENTLY ASKED QUESTIONS
Questions About Statistical Analysis for Finance Research
Short answers to what finance researchers ask before sharing a study, from method choice and heavy-tailed returns to backtest bias, data extracts and confidentiality.
What statistical methods are used in finance research?
Finance research most often uses panel regressions with firm and time effects, return models estimated by regression with robust or dependence-consistent errors, event-study methods, models for volatility in return series, and survival or logistic models for default and delisting. The choice follows the unit of observation and the dependence in the data. Our Statistical Methods pages describe each family.
Can I use ordinary least squares on stock returns?
Yes as an estimator, but not with default inference. Least squares can still estimate the average relationship, while heavy tails, changing volatility and correlation across securities make default standard errors unreliable and extreme observations influential. We use errors that match the dependence in the data, examine influential observations and report whether the result holds without them.
How do you guard a backtest against look-ahead and survivorship bias?
A backtest is only informative if every variable is dated as it was known and the sample includes entities that later disappeared. We check the timing of each input against its release date, keep delisted and merged funds or firms, include costs where the claim concerns a tradable strategy, and hold out a period that was not used to choose the specification. Even then, the result is reported as evidence in this sample, with the number of strategies tried disclosed.
Can you analyze extracts from licensed financial databases?
Yes, we analyze the extracts you provide, whatever database they come from. Before estimation we review the identifiers, the dating of variables and the treatment of delistings in the file. The license terms of the database, including what may be shared with others, remain your responsibility.
How is confidential financial research data handled?
Return files, database extracts and project details are handled confidentially, and project information is not published. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
You send your research question, the data you hold and any draft specification. A statistically trained Research Coordinator reviews them and responds the same day, and the analysis, deliverables, timeline and one fixed project fee are confirmed before work begins, so the timeline follows from the agreed scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Discuss Your
Finance Research With Us
Share your research question, the sample you plan to use and your current specification, even if only a draft exists. We review the timing of variables and the error structure and confirm the approach first.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014